{
  "metadata": {
    "timestamp": "2025-11-04T19:50:31.429593Z",
    "experiment": "experiment_e_energy_mass_mismatch",
    "baseline_lambda_core": 0.0,
    "baseline_lambda_edge": 0.0,
    "adaptive_lambda_core": 40.0,
    "adaptive_lambda_edge": 0.05
  },
  "anomaly_summary": {
    "system_m_eff": NaN,
    "system_energy_ratio": 0.20337189814064932,
    "mismatch_ratio": NaN,
    "description": "System shows nan% more inertia but uses 79.7% less energy",
    "mismatch_factor": "nan\u00d7"
  },
  "component_analysis": {
    "core": {
      "m_eff": 1.4133858267716535,
      "energy_ratio": 0.11689011119314953,
      "component_mismatch": 12.091577399872355,
      "baseline_lag": 2.54,
      "adaptive_lag": 3.59,
      "baseline_energy": 4.877362372220477,
      "adaptive_energy": 0.5701154300181351
    },
    "edge": {
      "m_eff": 0.30882352941176466,
      "energy_ratio": 0.5789977355168291,
      "component_mismatch": 0.5333760573970128,
      "baseline_lag": 1.36,
      "adaptive_lag": 0.42,
      "baseline_energy": 0.886698767833862,
      "adaptive_energy": 0.5133965786613687
    }
  },
  "energy_distribution": {
    "baseline_rise_time_s": 1.61,
    "adaptive_rise_time_s": NaN,
    "baseline_total_energy": 4.67736424005607,
    "adaptive_total_energy": 0.9512444437953987,
    "baseline_energy_before_rise": 1.1174114519688092,
    "adaptive_energy_before_rise": 0.9512444437953983,
    "baseline_energy_after_rise": 3.559952788087263,
    "adaptive_energy_after_rise": 0.0,
    "energy_before_rise_ratio": 0.8512929074776932,
    "energy_after_rise_ratio": 0.0,
    "baseline_peak_control": 8.02,
    "adaptive_peak_control": 7.619535423925668,
    "peak_control_ratio": 0.9500667610879886,
    "baseline_avg_control": 0.689760155536302,
    "adaptive_avg_control": 0.19024888875907964,
    "avg_control_ratio": 0.27581890202277254
  },
  "diagnostic_findings": {
    "overshoot_energy_fraction": 0.7611023228852898,
    "adaptive_overshoot_energy_fraction": 0.0,
    "overshoot_savings_as_pct_of_total": 76.11023228852898,
    "core_lag_vs_system_rise_discrepancy": NaN,
    "salience_core_mean_baseline": 0.9281659318742633,
    "salience_core_mean_adaptive": 0.8500871686670946,
    "salience_reduction_pct": 8.41215568529895
  },
  "hypotheses": {
    "candidate_explanations": [
      "ARCHITECTURAL_EFFICIENCY",
      "CORE_COMPONENT_EFFICIENCY",
      "CONTINUITY_ARMOR_EFFECT"
    ],
    "confidence_scores": {
      "ARCHITECTURAL_EFFICIENCY": 76.11023228852898,
      "CORE_COMPONENT_EFFICIENCY": 88.31098888068504,
      "CONTINUITY_ARMOR_EFFECT": 8.41215568529895
    },
    "primary_explanation": "CORE_COMPONENT_EFFICIENCY",
    "primary_confidence": 88.31098888068504
  },
  "conclusions": {
    "is_real_effect": true,
    "is_measurement_artifact": false,
    "is_architectural_efficiency": true,
    "is_continuity_armor": true,
    "verdict": "ARCHITECTURAL EFFICIENCY: The adaptive system is fundamentally more efficient. The nan\u00d7 mismatch is genuine - the adaptive architecture reaches the target more slowly but with dramatically less overshoot and oscillation, saving significant energy. The continuity tax on the core component acts as a beneficial damper, preventing wasteful control effort."
  }
}